Efficient Hand Pose Estimation from a Single Depth Image

Xu, Chi; Cheng, Li

doi:10.1109/iccv.2013.429

Cited by 158 publications

(146 citation statements)

References 15 publications

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“…There are a number of differences of our work here comparing to that of Xu and Cheng (2013): first, a simple two-step pipeline is utilized in our approach, in contrast to a more complicated approach in Xu and Cheng (2013) containing three steps. Second, in this work we attempt to consider random forest models that can be analyzed theoretically, while the random forest models in Xu and Cheng (2013) are not able to be studied theoretically. Third, there are also many other differences: the kinematic model parameters are estimated by a dynamically weighted scheme that leads to a significant error reduction in empirical evaluations.…”

Section: Related Workmentioning

confidence: 99%

“…In our approach, it is only used for filtering away noisy depth pixel observations during preprocessing. Although our empirical results in this paper are primarily based on Softkinetic TOF camera, we would like to point out that our approach works with generic depth cameras including TOF cameras as well as the structured illumination depth cameras such as Kinect Kinect (2011), where image denoising strategy of Xu and Cheng (2013) is adopted during preprocessing. Figure 2 presents a flowchart outlining our two-step approach.…”

Section: Top-down View Versus Frontal View) Each Image Involves Thrementioning

confidence: 99%

“…The pixel with low confidence value is treated as the background. For structured illumination cameras, the preprocessing strategy of Xu and Cheng (2013) can be applied. To obtain a hand image patch, a simple background removal technique similar to that of Shotton et al (2011) is adopted, followed by image cropping to obtain a hand-centered bounding box.…”

Section: Preprocessingmentioning

confidence: 99%

“…In its core lies the nevertheless challenging problem of 3D hand pose estimation (Erol et al 2007;Gorce et al 2011), owing mostly to the complex and dexterous nature of hand articulations (Gustus et al 2012). Facilitated by the emerging commodity-level depth cameras (Kinect 2011;Softkinetic 2012), recent efforts such as Keskin et al (2012) , Ye et al (2013), Xu and Cheng (2013), Tang et al (2014) have led to noticeable progress in the field. The problem is however still far from being satisfactorily solved: For example, not much quantitative analysis has been conducted on annotated real-world 3D datasets, partly due to the practical difficulty of setting up such testbeds.…”

Section: Introductionmentioning

confidence: 99%

“…An earlier version of our work appears in Xu and Cheng (2013) that deals with the problem of depth image-based hand pose estimation. There are a number of differences of our work here comparing to that of Xu and Cheng (2013): first, a simple two-step pipeline is utilized in our approach, in contrast to a more complicated approach in Xu and Cheng (2013) containing three steps.…”

Section: Related Workmentioning

confidence: 99%

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Estimate Hand Poses Efficiently from Single Depth Images

Nanjappa

Zhang

et al. 2015

Int J Comput Vis

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This paper aims to tackle the practically very challenging problem of efficient and accurate hand pose estimation from single depth images. A dedicated two-step regression forest pipeline is proposed: given an input hand depth image, step one involves mainly estimation of 3D location and in-plane rotation of the hand using a pixelwise regression forest. This is utilized in step two which delivers final hand estimation by a similar regression forest model based on the entire hand image patch. Moreover, our estimation is guided by internally executing a 3D hand kinematic chain model. For an unseen test image, the kinematic model parameters are estimated by a proposed dynamically weighted scheme. As a combined effect of these proposed building blocks, our approach is able to deliver more precise estimation of hand poses. In practice, our approach works at 15.6 frame-per-second (FPS) on an average laptop when implemented in CPU, which is further sped-up to 67.2 FPS when running on GPU. In addition, we introduce and make publicly available a data-glove annotated depth image dataset covering various hand shapes and gestures, which enables us conducting quantitative analyses on real-world hand images. The effectiveness of our approach is verified empirically on both synthetic and the annotated real-world datasets for hand pose estimation, as well as related applications including part-based labeling and gesture classification. In addition to empirical studies, the consistency property of our approach is also theoretically analyzed.

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Section: Related Workmentioning

confidence: 99%

Section: Top-down View Versus Frontal View) Each Image Involves Thrementioning

confidence: 99%

Section: Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Estimate Hand Poses Efficiently from Single Depth Images

Nanjappa

Zhang

et al. 2015

Int J Comput Vis

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References

2018

3D Shape Analysis

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Weakly-Supervised 3D Hand Pose Estimation from Monocular RGB Images

Cai

et al. 2018

Computer Vision – ECCV 2018

249

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Compared with depth-based 3D hand pose estimation, it is more challenging to infer 3D hand pose from monocular RGB images, due to substantial depth ambiguity and the difficulty of obtaining fullyannotated training data. Different from existing learning-based monocular RGB-input approaches that require accurate 3D annotations for training, we propose to leverage the depth images that can be easily obtained from commodity RGB-D cameras during training, while during testing we take only RGB inputs for 3D joint predictions. In this way, we alleviate the burden of the costly 3D annotations in real-world dataset. Particularly, we propose a weakly-supervised method, adaptating from fully-annotated synthetic dataset to weakly-labeled real-world dataset with the aid of a depth regularizer, which generates depth maps from predicted 3D pose and serves as weak supervision for 3D pose regression. Extensive experiments on benchmark datasets validate the effectiveness of the proposed depth regularizer in both weakly-supervised and fullysupervised settings.

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Efficient Hand Pose Estimation from a Single Depth Image

Cited by 158 publications

References 15 publications

Estimate Hand Poses Efficiently from Single Depth Images

Estimate Hand Poses Efficiently from Single Depth Images

References

Weakly-Supervised 3D Hand Pose Estimation from Monocular RGB Images

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